Isotropic cosmic birefringence from early dark energy

A tantalizing hint of isotropic cosmic birefringence has been found in the $E B$ cross-power spectrum of the cosmic microwave background (CMB) polarization data with a statistical significance of $3\sigma$. A pseudoscalar field coupled to the CMB photons via the Chern-Simons term can explain this observation. The same field may also be responsible for early dark energy (EDE), which alleviates the so-called Hubble tension. Since the EDE field evolves significantly during the recombination epoch, the conventional formula that relates $E B$ to the difference between the $E$- and $B$-mode auto-power spectra is no longer valid. Solving the Boltzmann equation for polarized photons and the dynamics of the EDE field consistently, we find that currently favored parameter space of the EDE model yields a variety of shapes of the $EB$ spectrum, which can be tested by CMB experiments.Comment: 6 pages, 3 figures, 1 tabl

Constraint on Early Dark Energy from Isotropic Cosmic Birefringence

Polarization of the cosmic microwave background (CMB) is sensitive to new physics violating parity symmetry, such as the presence of a pseudoscalar "axionlike" field. Such a field may be responsible for early dark energy (EDE), which is active prior to recombination and provides a solution to the so-called Hubble tension. The EDE field coupled to photons in a parity-violating manner would rotate the plane of linear polarization of the CMB and produce a cross-correlation power spectrum of $E$- and $B$-mode polarization fields with opposite parities. In this paper, we fit the $EB$ power spectrum predicted by the photon-axion coupling of the EDE model with a potential $V(\phi)\propto [1-\cos(\phi/f)]^3$ to polarization data from Planck. We find that the unique shape of the predicted $EB$ power spectrum is not favored by the data and obtain a first constraint on the photon-axion coupling constant, $g=(0.04\pm 0.16)M_{\text{Pl}}^{-1}$ (68% CL), for the EDE model that best fits the CMB and galaxy clustering data. This constraint is independent of the miscalibration of polarization angles of the instrument or the polarized Galactic foreground emission. Our limit on $g$ may have important implications for embedding EDE in fundamental physics, such as string theory.Comment: 7 pages, 3 figures, 1 table. The stacked EB power spectrum is publicly available at https://github.com/LilleJohs/Observed-EB-Power-Spectru

Syntenic analysis of ACCase loci and target-site-resistance mutations in cyhalofop-butyl resistant Echinochloa crus-galli var. crus-galli in Japan

BACKGROUND: Recently, suspected cyhalofop-butyl-resistant populations of allohexaploid weed Echinochloa crus-galli var. crus-galli were discovered in rice fields in Aichi Prefecture, Japan. Analyzing the target-site ACCase genes of cyhalofop-butyl helps understand the resistance mechanism. However, in E. crus-galli, the presence of multiple ACCase genes and the lack of detailed gene investigations have complicated the analysis of target-site genes. Therefore, in this study, we characterized the herbicide response of E. crus-galli lines and thoroughly characterized the ACCase genes, including the evaluation of gene mutations in the ACCase genes of each line. RESULT: Four suspected resistant lines collected from Aichi Prefecture showed varying degrees of resistance to cyhalofop-butyl and other FOP-class ACCase inhibitors but were sensitive to herbicides with other modes of action. Through genomic analysis, six ACCase loci were identified in the E. crus-galli genome. We renamed each gene based on its syntenic relationship with other ACCase genes in the Poaceae species. RNA-sequencing analysis revealed that all ACCase genes, except the pseudogenized copy ACCase2A, were transcribed at a similar level in the shoots of E. crus-galli. Mutations known to confer resistance to FOP-class herbicides, that is W1999C, W2027C/S and I2041N, were found in all resistant lines in either ACCase1A, ACCase1B or ACCase2C. CONCLUSION: In this study, we found that the E. crus-galli lines were resistant exclusively to ACCase-inhibiting herbicides, with a target-site resistance mutation in the ACCase gene. Characterization of ACCase loci in E. crus-galli provides a basis for further research on ACCase herbicide resistance in Echinochloa spp. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry

Albumin-conjugated PEG liposome enhances tumor distribution of liposomal doxorubicin in rats

To evaluate the effect of coupling of recombinant human serum albumin (rHSA) onto the surface of poly(ethylene glycol)-modified liposorne (PEG liposome) on the in vivo disposition characteristics of liposomal doxorubicin (DXR), the pharmacokinetics and tissue distribution of DXR were evaluated after intravenous administration of rHSA-modified PEG (rHSA/PEG) liposomal DXR into tumor-bearing rats. rHSA/PEG liposome prepared using a hetero-bifunctional cross-linker, N- succinimidyl 3-(2-pyridyldithio) propionate (SPDP), efficiently encapsulated DXR (over 95%). rHSA/PEG liposomal DXR showed longer blood-circulating property than PEG liposornal DXR and the hepatic and splenic clearances of rHSA/PEG liposornal DXR were significantly smaller than those of PEG liposomal DXR. It was also demonstrated that the disposition of DXR to the heart, one of the organs for DXR-related side-effects, was significantly smaller than free DXR. Furthermore, the tumor accumulation of rHSA/PEG liposomal DXR was significantly larger than that of PEG liposomal DXR. The &#34;therapeutic index&#34;, a criterion for therapeutic outcome, for rHSA/PEG fiposornal DXR was significantly higher than PEG liposomal DXR. These results clearly indicate that rHSA-conjugation onto the surface of PEG liposome would be a useful approach to increase the effectiveness and safety of PEG liposomal DXR.</p

Investigation of Methods to Create Future Multimodal Emotional Data for Robot Interactions in Patients with Schizophrenia : A Case Study

Rapid progress in humanoid robot investigations offers possibilities for improving the competencies of people with social disorders, although this improvement of humanoid robots remains unexplored for schizophrenic people. Methods for creating future multimodal emotional data for robot interactions were studied in this case study of a 40-year-old male patient with disorganized schizophrenia without comorbidities. The qualitative data included heart rate variability (HRV), video-audio recordings, and field notes. HRV, Haar cascade classifier (HCC), and Empath API© were evaluated during conversations between the patient and robot. Two expert nurses and one psychiatrist evaluated facial expressions. The research hypothesis questioned whether HRV, HCC, and Empath API© are useful for creating future multimodal emotional data about robot–patient interactions. The HRV analysis showed persistent sympathetic dominance, matching the human–robot conversational situation. The result of HCC was in agreement with that of human observation, in the case of rough consensus. In the case of observed results disagreed upon by experts, the HCC result was also different. However, emotional assessments by experts using Empath API© were also found to be inconsistent. We believe that with further investigation, a clearer identification of methods for multimodal emotional data for robot interactions can be achieved for patients with schizophrenia

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects

Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attributes within multiple drug–side effect heterogeneous graphs have not been completely exploited.Methods: We proposed a new drug-side effect association prediction method, GGSC, to deeply integrate the diverse topologies and attributes from multiple heterogeneous graphs and the self-calibration attributes of each drug-side effect node pair. First, we created two heterogeneous graphs comprising the drug and side effect nodes and their related similarity and association connections. Since each heterogeneous graph has its specific topology and node attributes, a node feature learning strategy was designed and the learning for each graph was enhanced from a graph generative and adversarial perspective. We constructed a generator based on a graph convolutional autoencoder to encode the topological structure and node attributes from the whole heterogeneous graph and then generate the node features embedding the graph topology. A discriminator based on multilayer perceptron was designed to distinguish the generated topological features from the original ones. We also designed representation-level attention to discriminate the contributions of topological representations from multiple heterogeneous graphs and adaptively fused them. Finally, we constructed a self-calibration module based on convolutional neural networks to guide pairwise attribute learning through the features of the small latent space.Results: The comparison experiment results showed that GGSC had higher prediction performance than several state-of-the-art prediction methods. The ablation experiments demonstrated the effectiveness of topological enhancement learning, representation-level attention, and self-calibrated pairwise attribute learning. In addition, case studies over five drugs demonstrated GGSC’s ability in discovering the potential drug-related side effect candidates.Conclusion: We proposed a drug-side effect association prediction method, and the method is beneficial for screening the reliable association candidates for the biologists to discover the actual associations

Polysaccharides as potential antioxidative compounds for extended-release matrix tablets.

The objective of this study was to identify polysaccharides with antioxidant properties for use as potential antioxidative compounds for extended-release matrix tablets. The antioxidant properties of five different polysaccharides, high molecular weight alginate (H-ALG), low molecular weight alginate (L-ALG), high molecular weight chitosan (H-chitosan), low molecular weight chitosan (L-chitosan), and pectic acid (PA) were examined using N-centered radicals from 1,1\u27-diphenyl-2-picrylhydrazyl (DPPH) and 2,2\u27-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and reducing power, based on their ability to reduce Cu(2+). L-chitosan and PA had acceptable scavenging abilities and were good radical scavengers, with good reducing power, but the H-chitosan and alginate derivatives were much less effective. The results suggest that L-chitosan and PA could be useful in combating oxidative stress. A PA and L-chitosan interpolymer complex (IPC) tablet was prepared and evaluated as an extended-release tablet matrix using theophylline (TPH) as a model drug. The release of TPH from the matrix tablet (TPH/PA/L-chitosan=200 mg:150 mg:50 mg) was slower than that from PA only (TPH/PA/chitosans=200 mg:200 mg:0 mg) or L-chitosan only (TPH/PA/L-chitosan=200 mg:0 mg:200 mg) tablet. Turbidity measurements also indicated the optimum complexation ratio for IPC between PA/L-chitosan to be 1/3, indicating an acceptable relationship between the turbidity of the complex and the release ratio of TPH. These results suggest that an L-chitosan/PA complex would be potentially useful in an extended-release IPC tablet with high antioxidant activity.The objective of this study was to identify polysaccharides with antioxidant properties for use as potential antioxidative compounds for extended-release matrix tablets. The antioxidant properties of five different polysaccharides, high molecular weight alginate (H-ALG), low molecular weight alginate (L-ALG), high molecular weight chitosan (H-chitosan), low molecular weight chitosan (L-chitosan), and pectic acid (PA) were examined using N-centered radicals from 1,1\u27-diphenyl-2-picrylhydrazyl (DPPH) and 2,2\u27-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and reducing power, based on their ability to reduce Cu(2+). L-chitosan and PA had acceptable scavenging abilities and were good radical scavengers, with good reducing power, but the H-chitosan and alginate derivatives were much less effective. The results suggest that L-chitosan and PA could be useful in combating oxidative stress. A PA and L-chitosan interpolymer complex (IPC) tablet was prepared and evaluated as an extended-release tablet matrix using theophylline (TPH) as a model drug. The release of TPH from the matrix tablet (TPH/PA/L-chitosan=200 mg:150 mg:50 mg) was slower than that from PA only (TPH/PA/chitosans=200 mg:200 mg:0 mg) or L-chitosan only (TPH/PA/L-chitosan=200 mg:0 mg:200 mg) tablet. Turbidity measurements also indicated the optimum complexation ratio for IPC between PA/L-chitosan to be 1/3, indicating an acceptable relationship between the turbidity of the complex and the release ratio of TPH. These results suggest that an L-chitosan/PA complex would be potentially useful in an extended-release IPC tablet with high antioxidant activity

Comparison of Subjective Facial Emotion Recognition and “Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection” between Patients with Schizophrenia and Healthy Participants

Patients with schizophrenia may exhibit a flat affect and poor facial expressions. This study aimed to compare subjective facial emotion recognition (FER) and FER based on multi-task cascaded convolutional network (MTCNN) face detection in 31 patients with schizophrenia (patient group) and 40 healthy participants (healthy participant group). A Pepper Robot was used to converse with the 71 aforementioned participants; these conversations were recorded on video. Subjective FER (assigned by medical experts based on video recordings) and FER based on MTCNN face detection was used to understand facial expressions during conversations. This study confirmed the discriminant accuracy of the FER based on MTCNN face detection. The analysis of the smiles of healthy participants revealed that the kappa coefficients of subjective FER (by six examiners) and FER based on MTCNN face detection concurred (κ = 0.63). The perfect agreement rate between the subjective FER (by three medical experts) and FER based on MTCNN face detection in the patient, and healthy participant groups were analyzed using Fisher’s exact probability test where no significant difference was observed (p = 0.72). The validity and reliability were assessed by comparing the subjective FER and FER based on MTCNN face detection. The reliability coefficient of FER based on MTCNN face detection was low for both the patient and healthy participant groups

A cell-based high-throughput screening method to directly examine transthyretin amyloid fibril formation at neutral pH

Transthyretin (TTR) is a major amyloidogenic protein associated with hereditary (ATTRm) and nonhereditary (ATTRwt) intractable systemic transthyretin amyloidosis. The pathological mechanisms of ATTR-associated amyloid fibril formation are incompletely understood, and there is a need for identifying compounds that target ATTR. C-terminal TTR fragments are often present in amyloid-laden tissues of most patients with ATTR amyloidosis, and on the basis of in vitro studies, these fragments have been proposed to play important roles in amyloid formation. Here, we found that experimentally-formed aggregates of full-length TTR are cleaved into C-terminal fragments, which were also identified in patients' amyloid-laden tissues and in SH-SY5Y neuronal and U87MG glial cells. We observed that a 5-kDa C-terminal fragment of TTR, TTR81–127, is highly amyloidogenic in vitro, even at neutral pH. This fragment formed amyloid deposits and induced apoptosis and inflammatory gene expression also in cultured cells. Using the highly amyloidogenic TTR81–127 fragment, we developed a cell-based high-throughput screening method to discover compounds that disrupt TTR amyloid fibrils. Screening a library of 1280 off-patent drugs, we identified two candidate repositioning drugs, pyrvinium pamoate and apomorphine hydrochloride. Both drugs disrupted patient-derived TTR amyloid fibrils ex vivo, and pyrvinium pamoate also stabilized the tetrameric structure of TTR ex vivo in patient plasma. We conclude that our TTR81–127–based screening method is very useful for discovering therapeutic drugs that directly disrupt amyloid fibrils. We propose that repositioning pyrvinium pamoate and apomorphine hydrochloride as TTR amyloid-disrupting agents may enable evaluation of their clinical utility for managing ATTR amyloidosis